AI development is moving fast, but the systems responsible for tracking the data behind it need to keep up. Think about what happens before an AI model is trained. Large volumes of information have to be sourced, organized, and evaluated. Developers need to understand what their datasets contain, where the material originated, and what conditions apply to its use. Doing this at scale presents a serious challenge. When data records are incomplete or difficult to verify, developers can struggle to establish trust in their datasets. Contributors, meanwhile, may have little insight into what happens to the information they provide. This is an infrastructure problem, and solving it requires more than simply collecting additional data. That is the direction DATA Foundation is exploring with its DATA Network and Trace registry. Trace is designed to create a record of data contributions and their provenance, helping make information about data origins and associated rights easier to inspect. The next development is the integration of @datafdn Network and Trace into the @SuiNetwork ecosystem. Sui brings the blockchain infrastructure, while Walrus is set to serve as the default storage layer for DATA Foundation applications. Each component has a distinct role in the broader approach recording information about data contributions, providing blockchain infrastructure, and supporting decentralized storage. The goal is to build a stronger foundation for tracking data throughout the AI supply chain. Of course, records alone cannot guarantee that every dataset has the necessary permissions. Those permissions still need to be established and respected. But better infrastructure can make provenance easier to verify and help developers make more informed decisions about the data they use. As AI continues to evolve, the infrastructure supporting its data economy deserves just as much attention as the models themselves. #DATAonSuiQuest